90 citations · 160 across the 10 of their papers we have counts for
10 papers
Learning Generalizable Language-Conditioned Cloth Manipulation from Long Demonstrations
Hanyi Zhao, Jinxuan Zhu, Zihao Yan +3
Multi-step cloth manipulation is a challenging problem for robots due to the high-dimensional state spaces and the dynamics of cloth. Despite recent significant advances in end-to-…
GSON: A Group-based Social Navigation Framework with Large Multimodal Model
Shangyi Luo, Peng Sun, Ji Zhu +4
With the increasing presence of service robots and autonomous vehicles in human environments, navigation systems need to evolve beyond simple destination reach to incorporate socia…
Learning visual-based deformable object rearrangement with local graph neural networks
Yuhong Deng, Xueqian Wang, Lipeng chen
Goal-conditioned rearrangement of deformable objects (e.g. straightening a rope and folding a cloth) is one of the most common deformable manipulation tasks, where the robot needs…
Learning Language-Conditioned Deformable Object Manipulation with Graph Dynamics
Yuhong Deng, Kai Mo, Chongkun Xia +1
Multi-task learning of deformable object manipulation is a challenging problem in robot manipulation. Most previous works address this problem in a goal-conditioned way and adapt g…
Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment
Yuhong Deng, Xiaofeng Guo, Yixuan Wei +5
In this paper, a novel robotic grasping system is established to automatically pick up objects in cluttered scenes. A composite robotic hand composed of a suction cup and a gripper…
Deep Reinforcement Learning Based on Local GNN for Goal-conditioned Deformable Object Rearranging
Yuhong Deng, Chongkun Xia, Xueqian Wang +1
Object rearranging is one of the most common deformable manipulation tasks, where the robot needs to rearrange a deformable object into a goal configuration. Previous studies focus…
Graph-Transporter: A Graph-based Learning Method for Goal-Conditioned Deformable Object Rearranging Task
Yuhong Deng, Chongkun Xia, Xueqian Wang +1
Rearranging deformable objects is a long-standing challenge in robotic manipulation for the high dimensionality of configuration space and the complex dynamics of deformable object…
Foldsformer: Learning Sequential Multi-Step Cloth Manipulation With Space-Time Attention
Kai Mo, Chongkun Xia, Xueqian Wang +3
Sequential multi-step cloth manipulation is a challenging problem in robotic manipulation, requiring a robot to perceive the cloth state and plan a sequence of chained actions lead…
Scene Graph for Embodied Exploration in Cluttered Scenario
Yuhong Deng, Qie Sima, Di Guo +3
The ability to handle objects in cluttered environment has been long anticipated by robotic community. However, most of works merely focus on manipulation instead of rendering hidd…
MQA: Answering the Question via Robotic Manipulation
Yuhong Deng, Di Guo, Xiaofeng Guo +3
In this paper, we propose a novel task, Manipulation Question Answering (MQA), where the robot performs manipulation actions to change the environment in order to answer a given qu…